2011 IEEE International Conference on Robotics and Automation 2011
DOI: 10.1109/icra.2011.5979537
|View full text |Cite
|
Sign up to set email alerts
|

Upper-body kinesthetic teaching of a free-standing humanoid robot

Abstract: Abstract-We present an integrated approach allowing a free-standing humanoid robot to acquire new motor skills by kinesthetic teaching. The proposed method controls simultaneously the upper and lower body of the robot with different control strategies. Imitation learning is used for training the upper body of the humanoid robot via kinesthetic teaching, while at the same time Reaction Null Space method is used for keeping the balance of the robot. During demonstration, a force/torque sensor is used to record t… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
43
0

Year Published

2011
2011
2018
2018

Publication Types

Select...
6
4

Relationship

2
8

Authors

Journals

citations
Cited by 61 publications
(47 citation statements)
references
References 23 publications
1
43
0
Order By: Relevance
“…Machine learning has been successfully used before for learning tasks on bipedal robots, such as dynamic balancing, or even in tasks involving also the upper body such as learning to clean vertical surfaces [6]. One especially promising approach for autonomous robot learning is reinforcement learning (RL), as demonstrated in [7]- [11].…”
Section: A Related Workmentioning
confidence: 99%
“…Machine learning has been successfully used before for learning tasks on bipedal robots, such as dynamic balancing, or even in tasks involving also the upper body such as learning to clean vertical surfaces [6]. One especially promising approach for autonomous robot learning is reinforcement learning (RL), as demonstrated in [7]- [11].…”
Section: A Related Workmentioning
confidence: 99%
“…Recently, researchers have shown interest in studying in-contact skills. In-contact tasks which have been learned from demonstration include cleaning a vertical surface [2], controlling stiffness [3], ball-in-box [4], pouring drink [4], box pulling [5], flipping task [5], stapling [6], and grasping small objects [7]. However, all these studies aim at learning a policy merely to reproduce the demonstrated force.…”
Section: Reinforcement Learning For Improving Imitated In-contact Skillsmentioning
confidence: 99%
“…drawing workspace are computed by demonstrating a series of drawing movements by accompanying the manipulator along the drawing surface through kinesthetic teaching [33]. The trajectory to be tracked by the robot is then computed withx(t) = Ap(t).…”
Section: Robot Controlmentioning
confidence: 99%